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awesome-gnn-research's Introduction
![Awesome](https://camo.githubusercontent.com/50cf39121274b3db22bf1bd72cbe25af9078e037441cb5b5bdef1cc9dc5eb2f7/68747470733a2f2f63646e2e7261776769742e636f6d2f73696e647265736f726875732f617765736f6d652f643733303566333864323966656437386661383536353265336136336531353464643865383832392f6d656469612f62616467652e737667)
- arXiv'21 Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender Systems [Paper] [Code] [Link]
1.1 Efficient and Scalable GNN Architectures
- arXiv'21 GIST: Distributed Training for Large-Scale Graph Convolutional Networks [Paper] [Code] [Link]
- arXiv'21 Graph Learning with 1D Convolutions on Random Walks [Paper] [Code] [Link]
- ICML'19 Simplifying Graph Convolutional Networks [Paper] [Code] [Link]
- ICLR'19 Predict Then Propagate: Graph Neural Networks Meet Personalized PageRank [Paper] [Code] [Link]
- arXiv'21 Graph Attention Multi-Layer Perceptron [Paper] [Code] [Link]
- NeurIPS‘21 Node Dependent Local Smoothing for Scalable Graph Learning [Paper] [Code] [Link]
- ICLR'19 How Powerful are Graph Neural Networks? [Paper] [Code] [Link] √
- ICLR'22 A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?" [Paper] [No Code] [Link] √
1.2 Large-scale Graphs and Sampling Techniques
- NeurIPS'17 Inductive Representation Learning on Large Graphs [Paper] [Code] [Link]
- ICLR'18 FASTGCN: Fast Learning With Graph Convolutional Networks Via Importance Sampling [Paper] [Code] [Link]
- arXiv'21 GIST: Distributed Training for Large-Scale Graph Convolutional Networks [Paper] [Code] [Link]
1.3 Knowledge Distillation for GNNs
1.4 Neural Architecture Search for GNNs
1.5 Industrial Applications and Systems
1.6 Transfer Learning of GNNs
1.8 Graph Embedding Based on Random Walk √
- KDD'14 DeepWalk: Online Learning of Social Representations [Paper] [Code] [Link]
- WWW'15 LINE: Large-scale Information Network Embedding [Paper] [Code] [Link]
- KDD'16 node2vec: Scalable Feature Learning for Networks [Paper] [Code] [Link]
- NeurIPS'13 Distributed Representations of Words and Phrases and their Compositionality [Paper] [Code] [Link]
- KDD'16 Structural Deep Network Embedding [Paper] [Code] [Link]
- arXiv'21 Graph Learning with 1D Convolutions on Random Walks [Paper] [Code] [Link]
1.9 Non-IID and Graph Data Adaptive Augmentation
- arXiv'20 Non-Local Graph Neural Networks [Paper] [No Code] [Link] √
- WSDM'21 GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks [Paper] [Code] [Link] √
- IJCAI'21 Multi-Class Imbalanced Graph Convolutional Network Learning [Paper] [Code] [Link] √
- WWW'21 Graph Contrastive Learning with Adaptive Augmentation [Paper] [Code] [Link] √
- AAAI'21 Data Augmentation for Graph Neural Networks [Paper] [Code] [Link] √
- AAAI'22 Regularizing Graph Neural Networks via Consistency-Diversity Graph Augmentations [Paper] [No Code] [Link] ■
- KDD'20 NodeAug: Semi-Supervised Node Classification with Data Augmentation [Paper] [No Code] [https://zhuanlan.zhihu.com/p/466885671] ■
2. GNN + (Local) Differential Privacy
- introduction [Link]
- Local Differential Privacy: a tutorial [Paper] [Link]
- 本地化差分隐私研究综述 [Paper] [Link]
- 差分隐私 -- Laplace mechanism、Gaussian mechanism、Composition theorem [Link]
- 矩母函数 GMF 及矩的概念 -- 期望、方差、归一化矩、偏态、峰度 [Link] [Reference]
- Moments Accountant 的理解 [Link] [Reference]
- 基于 GNN 的隐私计算(差分隐私)Review(一)[Link]
2.2 Important Algorithms (Principles and Framework)
- SIGSAC'16 Deep Learning with Differential Privacy [Paper] [Code] [Link]
- ICLR'17 Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data [Paper] [Code] [Link]
- ICLR'18 Scalable Private Learning With PATE [Paper] [Code] [Link]
2.3 DP with Generative Model (Graph Generation)
- IJCAI'21 Secure Deep Graph Generation with Link Differential Privacy [Paper] [Code] [Link]
2.4 DP/LDP with Graph representation learning
- CCS'21 Locally Private Graph Neural Networks [Paper] [Code] [Link]
- arXiv'20 When Differential Privacy Meets Graph Neural Networks [Paper] [Code] [Link]
- arXiv'21 Releasing Graph Neural Networks with Differential Privacy [Paper] [No Code] [Link]
3. Federated Learning Based on GNN
3.1 Summary and Algorithms
- arXiv'21 Federated Graph Learning - A Position Paper [Paper] [Link]
- Big Data'19 SGNN: A Graph Neural Network Based Federated Learning Approach by Hiding Structure [Paper] [No Code] [Link]
- 基于 GNN 的隐私计算(联邦学习)Review(二)[Link]
- 基于 GNN 的隐私计算(联邦学习)Review(三)[Link]
- ICML'21 SpreadGNN: Serverless Multi task Federated Learning for Graph Neural Networks [Paper] [Code] [Link] ($\star$) ▲
- NeurIPS'21 Federated Graph Classification over Non-IID Graphs [Paper] [Code] [Link] √
- arXiv'20 Federated Dynamic GNN with Secure Aggregation [Paper] [No Code] [Link] ($ \star$) ■
3.3 Horizontal Intra-graph FL
- NeurIPS'21 Subgraph Federated Learning with Missing Neighbor Generation [Paper] [Code] [Link] √
- ICML'21 FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation [Paper] [No Code] [Link] ($\star$) √
- arXiv'21 FedGL: Federated Graph Learning Framework with Global Self-Supervision [Paper] [No Code] [Link] ($\star$) √
- PPNA'21 ASFGNN: Automated Separated-Federated Graph Neural Network [Paper] [No Code] [Link] ($\star$) ▲
- TSIPN'21 Distributed Training of Graph Convolutional Networks [Paper] [No Code] [Link] √
- KDD'21 Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling [Paper] [Code] [Link] ■
3.4 Vertical Intra-graph FL
- arXiv'21 A Vertical Federated Learning Framework for Graph Convolutional Network [Paper] [No Code] [Link] ($\star$) ■
- arXiv'21 Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification [Paper] [No Code] [Link] ($\star$) ▲
- CIKM'21 Federated Knowledge Graphs Embedding [Paper] [Code] [Link] ($ \star $) ▲
- IJCAI'21 Decentralized Federated Graph Neural Networks [Paper] [No Code] [Link] ($\star$) √
- ICML'21 SpreadGNN: Serverless Multi task Federated Learning for Graph Neural Networks [Paper] [Code] [Link] ($\star$) ▲
- TSIPN'21 Distributed Training of Graph Convolutional Networks [Paper] [No Code] [Link] √
- arXiv'21 A Graph Federated Architecture with Privacy Preserving Learning [Paper] [No Code] [Link] ($ \star$) ▲
- KDD'21 Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling [Paper] [Code] [Link] ■
- CVPR'21 Cluster-driven Graph Federated Learning over Multiple Domains [Paper] [No Code] [Link] ▲
- arXiv'19 Peer-to-Peer Federated Learning on Graphs [Paper] [No Code] [Link] ■
3.6 Personalized Federated Learning
- ICML'21 Personalized Federated Learning using Hypernetworks [Paper] [Code] [Link] ▲
- arXiv'20 GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs [Paper] [No Code] [Link] ▲
3.7 GraphFL Benchmark System
- ICLR'21 FedGraphNN: A Federated Learning Benchmark System for Graph Neural Networks [Paper] [Code] [Link] ■
- Federated Machine Learning: Concept and Applications [Paper] [Link]
- JMLR'17 Communication-Efficient Learning of Deep Networks from Decentralized Data [Paper] [Code] [Link] FedAvg √
- arXiv'21 Federated Learning on Non-IID Data Silos: An Experimental Study [Paper] [Code] [Link] √
- AAAI'21 Addressing Class Imbalance in Federated Learning [Paper] [Code] [Link] √
- arXiv'20 Non-IID Graph Neural Networks [Paper] [No Code] [Link] ▲
4.2 Communication Efficiency
- arXiv'19 Detailed comparison of communication efficiency of split learning and federated learning [Paper] [Link] ▲
- Graph library -- PyG、GarphGallery [Link]
- Graph library -- DIG、AutoGL、CogDL [Link]
- PyTorch Geometric(一):数据加载 [Link]
- PyTorch Geometric(二):模型搭建 [Link]
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